Literature DB >> 3841014

Modeling the electrical behavior of anatomically complex neurons using a network analysis program: excitable membrane.

B Bunow, I Segev, J W Fleshman.   

Abstract

We present methods for using the general-purpose network analysis program, SPICE, to construct computer models of excitable membrane displaying Hodgkin-Huxley-like kinetics. The four non-linear partial differential equations of Hodgkin and Huxley (H-H; 1952) are implemented using electrical circuit elements. The H-H rate constants, alpha and beta, are approximated by polynomial functions rather than exponential functions, since the former are handled more efficiently by SPICE. The process of developing code to implement the H-H sodium conductance is described in detail. The Appendix contains a complete listing of the code required to simulate an H-H action potential. The behavior of models so constructed is validated by comparison with the space-clamped and propagating action potentials of Hodgkin and Huxley. SPICE models of multiply branched axons were tested and found to behave as predicted by previous numerical solutions for propagation in inhomogeneous axons. New results are presented for two cases. First, a detailed, anatomically based model is constructed of group Ia input to an alpha-motoneuron with an excitable soma, a myelinated axon and passive dendrites. Second, we simulate interactions among clusters of mixed excitable and passive dendritic spines on an idealized neuron. The methods presented in this paper and its companion (Segev et al. 1985) should permit neurobiologists to construct and explore models which simulate much more closely the real morphological and physiological characteristics of nerve cells.

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Year:  1985        PMID: 3841014     DOI: 10.1007/bf00355689

Source DB:  PubMed          Journal:  Biol Cybern        ISSN: 0340-1200            Impact factor:   2.086


  53 in total

1.  Axo-somatic and axo-dendritic synapses of the cerebral cortex: an electron microscope study.

Authors:  E G GRAY
Journal:  J Anat       Date:  1959-10       Impact factor: 2.610

2.  [Impulse propagation in a model of a non-uniform nerve fiber. 3. Interaction of impulses in the area of a branching node of a nerve fiber].

Authors:  V F Pastushenko; V S Markin; Iu A Chizmadzhev
Journal:  Biofizika       Date:  1969 Sep-Oct

3.  Retinal ganglion cells: a functional interpretation of dendritic morphology.

Authors:  C Koch; T Poggio; V Torre
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  1982-07-27       Impact factor: 6.237

4.  Differential conduction block in branches of a bifurcating axon.

Authors:  Y Grossman; I Parnas; M E Spira
Journal:  J Physiol       Date:  1979-10       Impact factor: 5.182

5.  Passive cable properties of dendritic spines and spiny neurons.

Authors:  C J Wilson
Journal:  J Neurosci       Date:  1984-01       Impact factor: 6.167

6.  Three-dimensional structure of dendritic spines in the rat neostriatum.

Authors:  C J Wilson; P M Groves; S T Kitai; J C Linder
Journal:  J Neurosci       Date:  1983-02       Impact factor: 6.167

Review 7.  Modulation of impulse conduction along the axonal tree.

Authors:  H A Swadlow; J D Kocsis; S G Waxman
Journal:  Annu Rev Biophys Bioeng       Date:  1980

8.  Voltage clamp of cat motoneurone somata: properties of the fast inward current.

Authors:  J N Barrett; W E Crill
Journal:  J Physiol       Date:  1980-07       Impact factor: 5.182

9.  Electrotonic properties of neurons: steady-state compartmental model.

Authors:  D H Perkel; B Mulloney
Journal:  J Neurophysiol       Date:  1978-05       Impact factor: 2.714

10.  Distinguishing theoretical synaptic potentials computed for different soma-dendritic distributions of synaptic input.

Authors:  W Rall
Journal:  J Neurophysiol       Date:  1967-09       Impact factor: 2.714

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  11 in total

1.  Simulation of action potential propagation in complex terminal arborizations.

Authors:  H R Lüscher; J S Shiner
Journal:  Biophys J       Date:  1990-12       Impact factor: 4.033

2.  Computation of action potential propagation and presynaptic bouton activation in terminal arborizations of different geometries.

Authors:  H R Lüscher; J S Shiner
Journal:  Biophys J       Date:  1990-12       Impact factor: 4.033

3.  The propagation potential. An axonal response with implications for scalp-recorded EEG.

Authors:  A P Rudell; S E Fox
Journal:  Biophys J       Date:  1991-09       Impact factor: 4.033

Review 4.  Is realistic neuronal modeling realistic?

Authors:  Mara Almog; Alon Korngreen
Journal:  J Neurophysiol       Date:  2016-08-17       Impact factor: 2.714

5.  Solution of the Hodgkin-Huxley and cable equations on an array processor.

Authors:  N Stockbridge
Journal:  Ann Biomed Eng       Date:  1989       Impact factor: 3.934

6.  A continuous cable method for determining the transient potential in passive dendritic trees of known geometry.

Authors:  W R Holmes
Journal:  Biol Cybern       Date:  1986       Impact factor: 2.086

7.  Modeling the electrical behavior of anatomically complex neurons using a network analysis program: passive membrane.

Authors:  I Segev; J W Fleshman; J P Miller; B Bunow
Journal:  Biol Cybern       Date:  1985       Impact factor: 2.086

8.  Physiology, morphology and detailed passive models of guinea-pig cerebellar Purkinje cells.

Authors:  M Rapp; I Segev; Y Yarom
Journal:  J Physiol       Date:  1994-01-01       Impact factor: 5.182

9.  Realistic simulations of neurons by means of an ad hoc modified version of SPICE.

Authors:  M Bove; G Massobrio; S Martinoia; M Grattarola
Journal:  Biol Cybern       Date:  1994       Impact factor: 2.086

10.  PyMOOSE: Interoperable Scripting in Python for MOOSE.

Authors:  Subhasis Ray; Upinder S Bhalla
Journal:  Front Neuroinform       Date:  2008-12-19       Impact factor: 4.081

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